Overfitting & Regularization (L1/L2)

Summary

Overfitting is memorizing training data instead of learning general patterns; L1 (Lasso) and L2 (Ridge) regularization penalize large weights to keep models simpler and more general.

How to Learn This

1Deliberately overfit a model on a small dataset to see the symptom clearly.
2Apply L1 and L2 regularization and compare their effect on model coefficients.
3Learn why L1 tends to zero out weights (feature selection) while L2 shrinks them smoothly.

Bias-Variance Tradeoff

Neural Network Basics

More topics in Feature Engineering & Model Evaluation

Feature Scaling & Normalization One-Hot & Label Encoding Feature Selection Handling Imbalanced Data Hyperparameter Tuning (Grid/Random Search)Ensemble Methods (Bagging, Boosting)XGBoost & LightGBM Evaluation Metrics (Accuracy, Precision, Recall, F1)ROC-AUC Confusion Matrix Bias-Variance Tradeoff

Stuck on this topic? Ask an Insider

Get 1:1 guidance from people who've walked this exact path — free on the InsideEdge app.

Download